1. Technical Field
This application relates to data processing systems for predicting an auction result, as well as determining auction result insurance parameters. In particular, this application relates to data processing systems that predict an auction result that may include price ranges and confidence measures that specify a confidence in a corresponding price range, as well as determining an insured auction result and insurance cost for the insured auction result.
2. Background Information
Rapid technology growth in recent years has brought widespread Internet access into the homes of millions of individuals. As a result, those individuals have access to convenient online auction services provided by such companies as eBay, uBid, and Yahoo. The immense popularity of online auction services is evident in the hundreds of thousands of auctions running simultaneously at any given time for everything from new flat panel monitors to upright arcade videogames from the early 80's.
Whether the auction is an online auction or a traditional auction, sellers are faced with the challenge of obtaining a satisfactory price for their auction item. In the past, sellers either accepted whatever final price was reached for the auction item, or set a reserve price or opening price to match at least the satisfactory price. Accepting the final price often resulted in an auction item selling for less than the satisfactory price, while setting a reserve or opening price sometimes failed to attract any buyers at all.
Online marketplaces have become significant commercial entities. It is estimated that these marketplaces, such as EBAY, YAHOO! SHOPPING AUCTIONS, and AMAZON.COM AUCTIONS, will account for 25 percent of ecommerce by 2005. Even today, EBAY, one of the largest online marketplaces, typically offers 19 million items for sale at any given time. In 2003, $24 billion of goods were sold on EBAY, which maintained a $57 billion market value and had $2.4 billion in revenue. Although online marketplaces such as EBAY offer these individuals a unique opportunity to sell their goods, the value for any given item sold at auction is inherently hard to predict for several reasons.
First, the condition of items can differ in many different ways, regardless of how similar the items are to one another. Buyers may be willing to pay a premium for new or like-new items, while items that exhibit years of wear and tear may command a lower price. Second, most online auctions offer a variety of shipping options. For example, the buyer may have to pay all shipping costs, the seller may assume all shipping costs, or the buyer and seller may split shipping costs. Thus, the total payment due to a seller may not only include the final purchase price, but also all, none, or some of the cost of shipping the item. Third, most online marketplaces have established selling rating systems to provide transaction feedback and increase buyer confidence. The seller rating may affect the value of a winning bid since, for example, a highly rated seller may attract additional bids, while a lower rated seller may not receive as many.
Fourth, item descriptions for substantially identical items item may vary dramatically. For example, some sellers may include images of the item, while others may not. The value of a winning bid may vary with the effectiveness of the item description, including the presence or absence of images. Fifth, each auction begins and ends at specified times. When coupled with the fact that 15% of auctions are won in the last minute, the timing of an auction can play a factor in the value of the winning bid. For example, the same item may command a higher premium at an auction that ends at 3:00 PM as opposed to an auction 3:00 AM. The difficulties in predicting the auction outcome may discourage both buyers and sellers from entering the online market. Among other concerns, sellers may not want to risk low winning bids, and buyers may not want to risk the silent bidding process that chases the item cost ever higher
In the past, few if any tools were available to analyze auctions. For example, the Andale.com website, created by Andale, Inc. of Mountain View, Calif., determines average item selling prices based on past auction data. Simple price averaging may provide a crude price approximation, but, based on price information alone, it is a relatively inaccurate estimator.
Accordingly, a need has long existed for methods and systems that may accurately predict an auction result and provide auction result insurance.